This paper proposes an approach for the segmentation of human body movements measured by inertial measurement unit sensors. Using the angular velocity and linear acceleration measurements directly, without converting to joint angles, we perform segmentation by formulating the problem as a classification problem, and training a classifier to differentiate between motion end-point and within-motion points. The proposed approach is validated with experiments measuring the upper body movement during reaching tasks, demonstrating classification accuracy of over 85.8%.